{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rnn-timeseries.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lmoroney/tfbook/blob/master/chapter11/rnn_timeseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1J15Vh_1Jih",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "ecbfee02-fe08-46c3-dd0f-c41201d0a3dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOjujz601HcS",
        "colab_type": "code",
        "outputId": "53af3578-150c-4596-81f0-11cfe31bcc53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {}
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(True)\n",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.1)  \n",
        "baseline = 10\n",
        "amplitude = 20\n",
        "slope = 0.09\n",
        "noise_level = 5\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=42)\n",
        "\n",
        "min = np.min(series)\n",
        "max = np.max(series)\n",
        "series -= min\n",
        "series /= max\n",
        "split_time = 1000\n",
        "\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qR-PmG-RMAX9",
        "outputId": "696bbdd1-b836-4451-d479-1fe3ed868b94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_valid, x_valid)\n",
        "#plot_series(time_train, x_train)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u2dKH3/vJa1Mfd1oXfQDC/UvDoD5hBHFh8Xy/7klovGhi/7kC9p8rpF6/kkLJ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\nEARBEASxDJAIIwiCIAiCWAb+f3Ghm9OhSkQGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sTTIOCbyShY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KrcN1prrw910",
        "colab_type": "code",
        "outputId": "c1ce6682-e79d-4c2d-a888-127524247d5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "tf.keras.backend.clear_session()\n",
        "tf.random.set_seed(51)\n",
        "np.random.seed(51)\n",
        "window_size = 15\n",
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),input_shape=[None]),\n",
        "  tf.keras.layers.SimpleRNN(100, input_shape=[None, 1], return_sequences=True, dropout=0.1, recurrent_dropout=0.5),\n",
        "  tf.keras.layers.SimpleRNN(100, return_sequences=True, dropout=0.1, recurrent_dropout=0.5, activation='relu'), \n",
        "  tf.keras.layers.Dense(1),\n",
        "])\n",
        "\n",
        "model.compile(loss='mae', optimizer=tf.keras.optimizers.RMSprop(), metrics=[\"mae\"])\n",
        "history = model.fit(dataset, epochs=100,  verbose=1)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "31/31 [==============================] - 2s 71ms/step - loss: 0.3072 - mae: 0.3079\n",
            "Epoch 2/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.1815 - mae: 0.1816\n",
            "Epoch 3/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.1548 - mae: 0.1549\n",
            "Epoch 4/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.1305 - mae: 0.1305\n",
            "Epoch 5/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.1184 - mae: 0.1184\n",
            "Epoch 6/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.1032 - mae: 0.1033\n",
            "Epoch 7/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0911 - mae: 0.0913\n",
            "Epoch 8/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0824 - mae: 0.0825\n",
            "Epoch 9/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0739 - mae: 0.0739\n",
            "Epoch 10/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0656 - mae: 0.0657\n",
            "Epoch 11/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0646 - mae: 0.0647\n",
            "Epoch 12/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0605 - mae: 0.0606\n",
            "Epoch 13/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0595 - mae: 0.0596\n",
            "Epoch 14/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0587 - mae: 0.0585\n",
            "Epoch 15/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0590 - mae: 0.0590\n",
            "Epoch 16/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0558 - mae: 0.0557\n",
            "Epoch 17/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0557 - mae: 0.0558\n",
            "Epoch 18/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0584 - mae: 0.0583\n",
            "Epoch 19/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0551 - mae: 0.0552\n",
            "Epoch 20/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0559 - mae: 0.0558\n",
            "Epoch 21/100\n",
            "31/31 [==============================] - 1s 25ms/step - loss: 0.0586 - mae: 0.0586\n",
            "Epoch 22/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0558 - mae: 0.0558\n",
            "Epoch 23/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0553 - mae: 0.0553\n",
            "Epoch 24/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0559 - mae: 0.0559\n",
            "Epoch 25/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0557 - mae: 0.0557\n",
            "Epoch 26/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0530 - mae: 0.0529\n",
            "Epoch 27/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0528 - mae: 0.0529\n",
            "Epoch 28/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0548 - mae: 0.0546\n",
            "Epoch 29/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0515 - mae: 0.0514\n",
            "Epoch 30/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0571 - mae: 0.0570\n",
            "Epoch 31/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0567 - mae: 0.0567\n",
            "Epoch 32/100\n",
            "31/31 [==============================] - 1s 29ms/step - loss: 0.0560 - mae: 0.0561\n",
            "Epoch 33/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0543 - mae: 0.0542\n",
            "Epoch 34/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0547 - mae: 0.0546\n",
            "Epoch 35/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0512 - mae: 0.0513\n",
            "Epoch 36/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0545 - mae: 0.0545\n",
            "Epoch 37/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0543 - mae: 0.0541\n",
            "Epoch 38/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0540 - mae: 0.0541\n",
            "Epoch 39/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0531 - mae: 0.0531\n",
            "Epoch 40/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0542 - mae: 0.0542\n",
            "Epoch 41/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0541 - mae: 0.0539\n",
            "Epoch 42/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0534 - mae: 0.0535\n",
            "Epoch 43/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0536 - mae: 0.0536\n",
            "Epoch 44/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0530 - mae: 0.0530\n",
            "Epoch 45/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0554 - mae: 0.0555\n",
            "Epoch 46/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0528 - mae: 0.0529\n",
            "Epoch 47/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0535 - mae: 0.0535\n",
            "Epoch 48/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0554 - mae: 0.0555\n",
            "Epoch 49/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0553 - mae: 0.0553\n",
            "Epoch 50/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0529 - mae: 0.0529\n",
            "Epoch 51/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0534 - mae: 0.0535\n",
            "Epoch 52/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0529 - mae: 0.0530\n",
            "Epoch 53/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0529 - mae: 0.0529\n",
            "Epoch 54/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0510 - mae: 0.0510\n",
            "Epoch 55/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0549 - mae: 0.0549\n",
            "Epoch 56/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0521 - mae: 0.0521\n",
            "Epoch 57/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0539 - mae: 0.0539\n",
            "Epoch 58/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0513 - mae: 0.0514\n",
            "Epoch 59/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0542 - mae: 0.0541\n",
            "Epoch 60/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0532 - mae: 0.0532\n",
            "Epoch 61/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0519 - mae: 0.0519\n",
            "Epoch 62/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0573 - mae: 0.0572\n",
            "Epoch 63/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0525 - mae: 0.0524\n",
            "Epoch 64/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0519 - mae: 0.0520\n",
            "Epoch 65/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0507 - mae: 0.0508\n",
            "Epoch 66/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0540 - mae: 0.0541\n",
            "Epoch 67/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0517 - mae: 0.0517\n",
            "Epoch 68/100\n",
            "31/31 [==============================] - 1s 25ms/step - loss: 0.0539 - mae: 0.0540\n",
            "Epoch 69/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0510 - mae: 0.0511\n",
            "Epoch 70/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0518 - mae: 0.0518\n",
            "Epoch 71/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0507 - mae: 0.0506\n",
            "Epoch 72/100\n",
            "31/31 [==============================] - 1s 25ms/step - loss: 0.0517 - mae: 0.0516\n",
            "Epoch 73/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0522 - mae: 0.0522\n",
            "Epoch 74/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0520 - mae: 0.0521\n",
            "Epoch 75/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0535 - mae: 0.0535\n",
            "Epoch 76/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0527 - mae: 0.0527\n",
            "Epoch 77/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0537 - mae: 0.0538\n",
            "Epoch 78/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0551 - mae: 0.0551\n",
            "Epoch 79/100\n",
            "31/31 [==============================] - 1s 25ms/step - loss: 0.0507 - mae: 0.0508\n",
            "Epoch 80/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0538 - mae: 0.0537\n",
            "Epoch 81/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0538 - mae: 0.0538\n",
            "Epoch 82/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0531 - mae: 0.0532\n",
            "Epoch 83/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0533 - mae: 0.0531\n",
            "Epoch 84/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0498 - mae: 0.0499\n",
            "Epoch 85/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0511 - mae: 0.0511\n",
            "Epoch 86/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0532 - mae: 0.0533\n",
            "Epoch 87/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0522 - mae: 0.0523\n",
            "Epoch 88/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0512 - mae: 0.0512\n",
            "Epoch 89/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0525 - mae: 0.0526\n",
            "Epoch 90/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0517 - mae: 0.0518\n",
            "Epoch 91/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0519 - mae: 0.0520\n",
            "Epoch 92/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0517 - mae: 0.0517\n",
            "Epoch 93/100\n",
            "31/31 [==============================] - 1s 28ms/step - loss: 0.0503 - mae: 0.0503\n",
            "Epoch 94/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0556 - mae: 0.0556\n",
            "Epoch 95/100\n",
            "31/31 [==============================] - 1s 25ms/step - loss: 0.0521 - mae: 0.0521\n",
            "Epoch 96/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0501 - mae: 0.0502\n",
            "Epoch 97/100\n",
            "31/31 [==============================] - 1s 27ms/step - loss: 0.0525 - mae: 0.0524\n",
            "Epoch 98/100\n",
            "31/31 [==============================] - 1s 25ms/step - loss: 0.0519 - mae: 0.0519\n",
            "Epoch 99/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0518 - mae: 0.0517\n",
            "Epoch 100/100\n",
            "31/31 [==============================] - 1s 26ms/step - loss: 0.0505 - mae: 0.0506\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_eaAX9g_jS5W",
        "colab": {}
      },
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
        "    ds = ds.batch(32).prefetch(1)\n",
        "    forecast = model.predict(ds)\n",
        "    return forecast"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKkic-mLdkRZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "forecast = model_forecast(model, series[split_time - window_size:-1], window_size)[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aa6tNADp5E12",
        "colab_type": "code",
        "outputId": "3ee84620-ca69-4754-aedb-0a65c2f10ef9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "#plot_series(time_valid, x_valid)\n",
        "#plot_series(time_valid, forecast)\n",
        "\n",
        "scaled_xvalid=x_valid*max\n",
        "scaled_xvalid=scaled_xvalid+min\n",
        "scaled_forecast=forecast*max\n",
        "scaled_forecast=scaled_forecast+min\n",
        "\n",
        "plot_series(time_valid, scaled_xvalid)\n",
        "plot_series(time_valid, scaled_forecast)\n",
        "\n",
        "print(max)\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "160.70773\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UTBs3RARZNEPmulyKKzVDduNabzvGmCcsLQer2rO491evwduv7MOmZWF3TN23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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kT6j186YO6K",
        "colab_type": "code",
        "outputId": "b9dff977-5251-467a-edce-f4e13c611631",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(scaled_xvalid, scaled_forecast).numpy()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([35.71196  , 24.23442  , 26.71287  , 31.329697 , 30.180393 ,\n",
              "       29.33767  , 39.323483 , 31.196878 , 33.050995 , 34.43038  ,\n",
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              "       15.859273 , 13.504347 , 14.757717 , 14.438511 , 11.9142275,\n",
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              "       12.966054 , 13.344003 , 12.264528 , 12.020555 , 14.239863 ,\n",
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              "       13.772335 , 11.830784 , 13.043755 , 13.850041 , 12.183526 ,\n",
              "       14.032692 , 12.717516 , 12.748619 , 11.706271 , 12.184218 ,\n",
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              "       14.5068655, 14.079746 , 12.9688015, 13.369399 , 12.78318  ,\n",
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              "       13.101035 , 13.028412 , 12.073977 , 11.480134 , 11.494789 ,\n",
              "       11.377594 , 11.500798 , 11.529878 , 11.617755 , 11.4337   ,\n",
              "       11.379365 , 11.448344 , 12.510686 , 11.420073 , 11.522669 ,\n",
              "       11.497051 , 11.962846 , 11.379848 , 12.018511 , 11.42162  ,\n",
              "       11.548082 ], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    }
  ]
}